THIS year in Chicago, some kids will get lead poisoning from the paint or pipes in their homes. Some restaurants will cook food in unsanitary conditions and, here and there, a street corner will be suddenly overrun with rats. These kinds of dangers are hard to avoid in a city of more than 2.5 million people. The problem is, no one knows for certain where or when they will pop up.

The Chicago city government is hoping to change that by knitting powerful predictive models into its everyday city inspections. Its latest project, currently in pilot tests, analyses factors such as home inspection records and census data, and uses the results to guess which buildings are likely to cause lead poisoning in children – a problem that affects around 500,000 children in the US each year. The idea is to identify trouble spots before kids are exposed to dangerous lead levels.

“The idea is to identify trouble spots before kids are exposed to dangerous lead levels”

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“We are able to prevent problems instead of just respond to them,” says Jay Bhatt, chief innovation officer at the Chicago Department of Public Health. “These models are just the beginning of the use of predictive analytics in public health and we are excited to be at the forefront of these efforts.”

Chicago’s projects are based on the thinking that cities already have what they need to raise their municipal IQ&colon; piles and piles of data. In 2012, city officials built WindyGrid, a platform that collected data like historical facts about buildings and up-to-date streams such as bus locations, tweets and 911 calls. The project was designed as a proof of concept and was never released publicly but it led to another, called Plenario, that allowed the public to access the data via an online portal.

The experience of building those tools has led to more practical applications. For example, one tool matches calls to the city’s municipal hotline complaining about rats with conditions that draw rats to a particular area, such as excessive moisture from a leaking pipe, or with an increase in complaints about garbage. This allows officials to proactively deploy sanitation crews to potential hotspots. It seems to be working&colon; last year, resident requests for rodent control dropped by 15 per cent.

Some predictions are trickier to get right. Charlie Catlett, director of the Urban Center for Computation and Data in Chicago, is investigating an old axiom among city cops&colon; that violent crime tends to spike when there’s a sudden jump in temperature. But he’s finding it difficult to test its validity in the absence of a plausible theory for why it might be the case. “For a lot of things about cities, we don’t have that underlying theory that tells us why cities work the way they do,” says Catlett.

Still, predictive modelling is maturing, as other cities succeed in using it to tackle urban ills. In 2011, New York City used it to pinpoint buildings with a higher than normal fire risk. The model focused on factors like missed tax or utility payments – which suggests neglect – and nearby crime and accident rates. By visiting only those buildings that the data pointed to, fire inspectors were able to find many more than if they’d simply gone door to door.

And many cities are exploring predictive policing, trying to calculate where crimes might happen based on where they have taken place in the past.

Such efforts can be a boon for cities, making them more productive, efficient and safe, says Rob Kitchin of Maynooth University in Ireland, who helped launched a real-time data site for Dublin last month called the Dublin Dashboard. But he cautions that there’s a limit to how far these systems can aid us. Knowing that a particular street corner is likely to be overrun with rats tomorrow doesn’t address what caused the infestation in the first place. “You might be able to create a sticking plaster or be able to manage it more efficiently, but you’re not going to be able to solve the deep structural problems.”

The dream of a smart city is alluring, says Michael Flowers, former Chief Analytics Officer for New York City, but he says a poorly executed algorithm could have disastrous effects. Imagine if a faulty model is used to decide who receives assistance in buying meals or childcare, he says.

“The technology is not going to make you any smarter,” Flowers says. “You have to be smart enough to know how to use the technology in the first place.”

This article appeared in print under the headline “How data can save a city”